Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks
This paper introduces learnable pairwise connections between filters in CNNs, moving beyond traditional pointwise nonlinearities. The proposed method allows the network to adapt connection functions per layer, improving accuracy.
[2606.13736] Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks
[Submitted on 11 Jun 2026]
Title:Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks
View a PDF of the paper titled Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks, by Kathleen Anderson and 2 other authors
View PDF HTML (experimental)
Abstract:While researchers continue to find new and improved network structures for CNNs, most of the newly invented architectures still rely on the traditional pattern of stacking convolutional blocks and separating them with pointwise activation functions. However, there are drawbacks to a network purely building on pointwise nonlinearities. One alternative is to introduce a pairwise connection between two filters of a network. Typical connection functions use multiplications or the minimum operation to realize logical AND connections. In this paper, we go one step further by demonstrating that CNNs can benefit from more general connections, which include parameters that are learned. With such parameters, the network is able to implement different connections in different network layers and better adapt the connection function to the task at hand.
Comments: IJCNN 2023
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2606.13736 [cs.CV]
(or arXiv:2606.13736v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.13736
arXiv-issued DOI via DataCite
Related DOI:
https://doi.org/10.1109/IJCNN54540.2023.10191082
DOI(s) linking to related resources
Submission history
From: Kathleen Anderson [view email] [v1] Thu, 11 Jun 2026 12:15:33 UTC (976 KB)
Full-text links:
Access Paper:
View a PDF of the paper titled Connections Between Pairs of Filters Improve the Accuracy of Convolutional Neural Networks, by Kathleen Anderson and 2 other authors
View PDF
HTML (experimental)
TeX Source
view license
Current browse context:
cs.CV
new | recent | 2026-06
Change to browse by:
cs
References & Citations
NASA ADS
Google Scholar
Semantic Scholar
Loading...
Data provided by:
Bibliographic Tools
Bibliographic and Citation Tools
Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
Connected Papers Toggle
Connected Papers (What is Connected Papers?)
Litmaps Toggle
Litmaps (What is Litmaps?)
scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
Code, Data, Media
Code, Data and Media Associated with this Article
alphaXiv Toggle
alphaXiv (What is alphaXiv?)
Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
DagsHub Toggle
DagsHub (What is DagsHub?)
GotitPub Toggle
Gotit.pub (What is GotitPub?)
Huggingface Toggle
Hugging Face (What is Huggingface?)
ScienceCast Toggle
ScienceCast (What is ScienceCast?)
Demos
Demos
Replicate Toggle
Replicate (What is Replicate?)
Spaces Toggle
Hugging Face Spaces (What is Spaces?)
Spaces Toggle
TXYZ.AI (What is TXYZ.AI?)
Related Papers
Recommenders and Search Tools
Link to Influence Flower
Influence Flower (What are Influence Flowers?)
Core recommender toggle
CORE Recommender (What is CORE?)
Author
Venue
Institution
Topic
About arXivLabs
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)